Course Outline

Introduction to AIASE

  • Overview of AI in software engineering
  • History and evolution of AIASE
  • Key concepts and terminology

AI Technologies in Software Development

  • Machine learning basics
  • Natural language processing (NLP) for code
  • Neural networks and deep learning models

Automating Software Development with AI

  • AI tools for generating boilerplate code
  • Automated code refactoring and optimization
  • Functional and unit test code generation
  • AI-assisted test case design and optimization

Enhancing Code Quality with AI

  • AI for bug detection and code reviews
  • Predictive analytics for software maintenance
  • AI-powered static and dynamic analysis tools
  • Automated debugging techniques
  • AI-driven fault localization and repair

AI in DevOps and Continuous Integration/Continuous Deployment (CI/CD)

  • AI for build optimization and deployment
  • AI in monitoring and log analysis
  • Predictive models for CI/CD pipelines
  • AI-based test automation in CI/CD workflows
  • AI for real-time error detection and resolution

AI for Documentation and Knowledge Management

  • Automated generation of docstrings and documentation
  • Knowledge extraction from codebases
  • AI for code search and reuse

Ethical Considerations and Challenges

  • Bias and fairness in AI tools
  • Intellectual property and licensing issues
  • Future of AI in software engineering

Hands-On Projects and Case Studies

  • Working with popular AI tools in software engineering
  • Case studies of AIASE in industry
  • Capstone project: Developing an AI-augmented software application

Summary and Next Steps

Requirements

  • An understanding of software development processes and methodologies
  • Experience with programming in Python
  • Basic knowledge of machine learning concepts

Audience

  • Software developers
  • Software engineers
  • Technical leads and managers
 14 Hours

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